The design of an information processing system can be approached on multiple levels. FIG. 1 illustrates two levels of abstraction, namely, the algorithm level and the physical circuit level. On the algorithmic level, one studies procedurally how the information is processed independently of the physical realization. The circuit level concerns the actual realization of the algorithm in physical hardware, for example, a biological neural circuit or silicon circuits in a digital signal processor.
Visual motion detection is important to the survival of animals. Many biological visual systems have evolved efficient/effective neural circuits to detect visual motion. Motion detection is performed in parallel with other visual coding circuits and starts already in the early stages of visual processing. In the retina of vertebrates, at least three types of Direction-Selective Ganglion Cells (DSGC) are responsible for signaling visual motion at this early stage. In flies, direction-selective neurons are found in the optic lobe, 3 synapses away from the photoreceptors.
The small number of synapses between photoreceptors and direction-selective neurons suggests that the processing involved in motion detection is not highly complex but still very effective. In addition, the biological motion detection circuits can be organized in a highly parallel way to enable fast, concurrent computation of motion. It is also interesting to note that the early stages of motion detection are carried out largely in the absence of spiking neurons, indicating that initial stages of motion detection are preferably performed in the “analog” domain. Taking advantage of continuous time processing can be important for quickly processing motion since motion intrinsically elicits fast and large changes in the intensity levels, that is, large amounts of data under stringent time constraints.
Certain computer-based motion detection algorithms employ optic flow techniques to estimate spatial changes in consecutive image frames. Although, often time, optic flow estimation algorithms produce accurate results, the computational demand to perform many of these algorithms can be too high for real-time implementation.
Several models for biological motion detection are known. For example, the Reichardt motion detector for motion detection in insects uses a correlation method to extract motion induced by spatiotemporal information patterns of light intensity. Therefore, it uses a correlation/multiplication operation. The motion energy detector uses spatiotemporal separable filters and a squaring nonlinearity to compute motion energy and it was shown to be equivalent to the Reichardt motion detector. Work in the rabbit retina was used for the Barlow-Levick model of motion detection, which uses inhibition to compensate motion in the null direction. However, there exists a need for an improved motion detection technique.